GPUStack: Open-Source GPU Cluster Management for High-Performance AI Serving

🚀 Want to deploy GPUStack yourself?

Docker configs, system requirements, and installation guides — all on one page.

Running AI models at scale used to mean fighting with GPU scheduling, driver hell, and half a dozen disjoint tools. GPUStack (Apache-2.0, ~5.5K stars) collapses that into a single open-source control plane: it pools every GPU across your machines into one cluster, then serves vLLM and SGLang models through a unified API — while also handing out on-demand, SSH-accessible GPU instances for training and development.

Why GPU Stacking Matters

Most teams either over-provision GPUs for peak load or let idle cards sit unused on developer machines. GPUStack solves both: it discovers heterogeneous GPUs (NVIDIA, AMD, Apple Silicon) automatically, schedules inference workloads on whichever cards are free, and shares capacity across the whole team. The result is dramatically better GPU utilization without buying new hardware.

What You Get Out of the Box

⚡ Unified Model Serving — Deploy OpenAI-compatible endpoints backed by vLLM or SGLang with autoscaling, model warm-up, and key-based access control.

🖥️ On-Demand GPU Instances — Allocate isolated, SSH-accessible GPU VMs for fine-tuning or dev work, then release them when done.

📦 One-Line Agent Install — Adding a new GPU node is a single command; the agent auto-registers with the server, no manual config.

GPUStack vs. Plain Docker GPU Serving

Capability	Docker + vLLM only	GPUStack
Multi-node pooling	❌ Manual	✅ Automatic
Autoscaling replicas	❌ No	✅ Built-in
SSH GPU instances	❌ No	✅ Yes
Multi-tenant access keys	❌ DIY	✅ Built-in

Built-In Model Catalog

GPUStack ships with a curated model catalog so you can deploy popular open-weight models — Llama, Qwen, DeepSeek, and more — in a few clicks, with automatic image pull and optimized serving configs.

Whether you are an MLOps engineer consolidating inference infrastructure or a startup trying to stretch a handful of GPUs, GPUStack is one of the most practical open-source options today. It is actively developed, well documented, and — thanks to its Apache-2.0 license — safe to build on.

🚀 Ready to unify your GPU fleet?

System requirements, Docker Compose configs, and full install guides on the tool page.

View GPUStack Tool Page →